Method and system for serving advertisements during streaming of dynamic, adaptive and non-sequentially assembled video

ABSTRACT

The present disclosure provides a system and method for serving one or more advertisements during streaming of dynamic, adaptive and non-sequentially assembled video. The method includes reception of a set of preference data and a set of user authentication data. The method includes fetching of the one or more tagged videos. The method includes fragmentation of each tagged video into the one or more tagged fragments. The method includes segregation of one or more mapped fragments into one or more logical sets of mapped fragments. The method includes mining of semantic context information from each mapped fragment. The method includes clustering of the one or more logical sets of mapped fragments into one or more logical clusters. The method includes allocation and insertion of one or more advertisements in one or more advertisement slots. The method includes assembling of the one or more logical clusters of mapped fragments.

TECHNICAL FIELD

The present disclosure relates to a field of online video streaming.More specifically, the present disclosure relates to a method and systemfor advertisement serving during streaming of an assembled video.

BACKGROUND

With the advent of online multimedia revolution along with sudden risein network bandwidth in recent years, the popularity of online video ondemand platforms has suddenly gained momentum. These video on demandplatforms provide a plethora of online streaming services. Theseservices include television news, sports shows, television shows,non-televised shows, interviews, location specific events, nationalevents, international events, movies and the like. The videos arearranged in different categories with different tags for complete video.Nowadays, there are many platforms that provide video assemblingservices on multiple on demand platforms. These platforms assemblevideos based on complete set of tags and don't take into accountdynamically changing user interests. In addition, these platforms don'tperform dynamic meta-tagging based context and ontology of searchqueries of users on fragments of videos. The present platforms areinefficient in providing personalized assembled videos to individualusers.

SUMMARY

In one aspect, the present disclosure provides a method for serving oneor more advertisements during streaming of dynamic, adaptive andnon-sequentially assembled video. The method includes a step ofreceiving a set of preference data associated with a user frompre-defined selection criteria and a set of user authentication data.The pre-defined selection criteria are associated with a digitallyprocessed repository of videos. Further, the method includes anotherstep of fetching the one or more tagged videos from the digitallyprocessed repository of videos. The one or more tagged videos arerelated to the set of preference data of the user. The method includesyet another step of fragmenting each tagged video of the one or moretagged videos into the one or more tagged fragments. Going further, themethod includes yet another step of segregating one or more mappedfragments of the one or more tagged fragments into one or more logicalsets of mapped fragments. The method includes yet another step of miningsemantic context information from each mapped fragment of the one ormore mapped fragments and each logical set of mapped fragments of theone or more logical sets of mapped fragments. Furthermore, the methodincludes yet another step of clustering the one or more logical sets ofmapped fragments into corresponding one or more logical clusters ofmapped fragments. The method includes yet another step of allocating oneor more advertisement slots in memory reserved for assembled video.Furthermore, the method includes yet another step of inserting at leastone type of the one or more advertisements in each allocatedadvertisement slot of the one or more advertisement slots. The methodincludes yet another step of assembling at least one of the one or morelogical clusters of mapped fragments and each of the one or moreadvertisements present in each advertisement slots. The assembled videois assembled in a pre-defined order of preference. Further, the one ormore tagged videos are fetched based on a correlation of a set of tagswith the set of preference data of the user. The set of tags isassociated with each tagged video of the one or more tagged videos.Furthermore, each tagged video is fragmented into the one or more taggedfragments. Each tagged fragment is characterized by a pre-determinedinterval of time. Accordingly, each tagged video is fragmented based onsegmentation of the tagged video for each pre-determined interval oftime. Going further, the one or more mapped fragments are segregatedbased on a positive mapping of keywords from the set of preference datawith the set of tags. The set of tags is associated with each taggedfragment of the one or more tagged fragments. The semantic contextinformation includes object specific context information and scenespecific context information of each mapped fragment and each logicalset of mapped fragments Moreover, the one or more advertisements areinserted based on mining of semantic context information, analysis ofthe set of preference data and a dynamic set of conditions. The one ormore advertisements are inserted in the one or more advertisement slotsin the real time. Each logical cluster of mapped fragments is assembledbased on the analysis of the set of preference data and the semanticcontext information.

In an embodiment of the present disclosure, the method further includesa step of creating a user profile corresponding to a received set ofuser authentication data and the set of preference data. The userprofile includes the set of preference data segregated on the basis ofpre-defined selection criteria and the set of user authentication data.In addition, the user profile includes the past set of preference data,a physical access location of the user and a bio data of the user.Moreover, the set of user authentication data includes an email address,a bio data of the user, an authentication key, a physical location and atime of request of video.

In an embodiment of the present disclosure, the method further includesa step of transcoding the assembled video into a pre-defined videoformat. The method utilizes a codec to transcode the assembled video.The assembled video is transcoded to enable adaptive bitrate streamingon each of the one or more communication devices of the user. Theadaptive bitrate streaming is based on one or more device parameters andone or more network parameters. The one or more device parametersinclude screen size, screen resolution and pixel density. The one ormore network parameters include an IP address, network bandwidth,maximum bitrate support over network, throughput, connection strengthand location of requesting server.

In an embodiment of the present disclosure, the method further includesa step of rendering the assembled video for addition of one or moreinteractive elements and bi-directional flow.

In an embodiment of the present disclosure, the method further includesa step of updating the assembled video in the digitally processedrepository of videos and a user profile of the user based on variationsin the set of preference data. In addition, the step includes updatingthe dynamic set of conditions and the set of authentication data in thereal time.

In an embodiment of the present disclosure, the dynamic set ofconditions includes a location associated with the user, user behaviorinformation, information associated with one or more communicationdevices and cookie information. In addition, the dynamic set ofconditions includes interests of the user and digital fingerprintinginformation.

In an embodiment of the present disclosure, the one or moreadvertisements are a type of video advertisement and a banneradvertisement. The video advertisement and the banner advertisement areinserted simultaneously. The one or more advertisement slots areallocated based on at least one of an advertisement skipping behavior ofthe user, a past interactivity of the user with the one or moreadvertisements and an advertisement preference manually selected by theuser.

In an embodiment of the present disclosure, the set of userauthentication data includes an email address, a bio-data of the user,an authentication key, a physical location, a standard time and a timezone of login.

In an embodiment of the present disclosure, the pre-defined selectioncriteria are based on date, time zone, day, season, physical location,occasion, identified name and video genre.

In an embodiment of the present disclosure, the pre-defined order ofpreference is derived from the set of preference data, the semanticcontext information, a user profile of the user and user profiles of anyother user having similar preferences.

In an embodiment of the present disclosure, each tagged video of the oneor more tagged videos is manually tagged by at least one of one or morepublishers.

In another embodiment of the present disclosure, each tagged video ofthe one or more tagged videos is manually tagged by at least one of oneor more system administrators.

In yet another embodiment of the present disclosure, each tagged videoof the one or more tagged videos is tagged based on voice instructionsof one or more system administrators.

In yet another embodiment of the present disclosure, each tagged videoof the one or more tagged videos is tagged based on audio rendering andanalysis.

In another aspect, the present disclosure provides a computer system.The computer system includes one or more processors and a memory. Thememory is coupled to the one or more processors. The memory is used tostore instructions. The instructions in the memory when executed by theone or more processors cause the one or more processors to perform amethod. The one or more processors perform the method for serving one ormore advertisements during streaming of dynamic, adaptive andnon-sequentially assembled video. The method includes a step ofreceiving a set of preference data associated with a user frompre-defined selection criteria and a set of user authentication data.The pre-defined selection criteria are associated with a digitallyprocessed repository of videos. Further, the method includes anotherstep of fetching the one or more tagged videos from the digitallyprocessed repository of videos. The one or more tagged videos arerelated to the set of preference data of the user. The method includesyet another step of fragmenting each tagged video of the one or moretagged videos into the one or more tagged fragments. Going further, themethod includes yet another step of segregating one or more mappedfragments of the one or more tagged fragments into one or more logicalsets of mapped fragments. The method includes yet another step of miningsemantic context information from each mapped fragment of the one ormore mapped fragments and each logical set of mapped fragments of theone or more logical sets of mapped fragments. Furthermore, the methodincludes yet another step of clustering the one or more logical sets ofmapped fragments into corresponding one or more logical clusters ofmapped fragments. The method includes yet another step of allocating oneor more advertisement slots in memory reserved for assembled video.Furthermore, the method includes yet another step of inserting at leastone type of the one or more advertisements in each allocatedadvertisement slot of the one or more advertisement slots. The methodincludes yet another step of assembling at least one of the one or morelogical clusters of mapped fragments and each of the one or moreadvertisements present in each advertisement slots. The assembled videois assembled in a pre-defined order of preference. Further, the one ormore tagged videos are fetched based on a correlation of a set of tagswith the set of preference data of the user. The set of tags isassociated with each tagged video of the one or more tagged videos.Furthermore, each tagged video is fragmented into the one or more taggedfragments. Each tagged fragment is characterized by a pre-determinedinterval of time. Accordingly, each tagged video is fragmented based onsegmentation of the tagged video for each pre-determined interval oftime. Going further, the one or more mapped fragments are segregatedbased on a positive mapping of keywords from the set of preference datawith the set of tags. The set of tags is associated with each taggedfragment of the one or more tagged fragments. The semantic contextinformation includes object specific context information and scenespecific context information of each mapped fragment and each logicalset of mapped fragments Moreover, the one or more advertisements areinserted based on mining of semantic context information, analysis ofthe set of preference data and a dynamic set of conditions. The one ormore advertisements are inserted in the one or more advertisement slotsin the real time. Each logical cluster of mapped fragments is assembledbased on the analysis of the set of preference data and the semanticcontext information.

In yet another aspect, the present disclosure provides acomputer-readable storage medium. The computer readable storage mediumenables encoding of computer executable instructions. The computerexecutable instructions when executed by at least one processor performa method. The at least one processor performs the method for serving oneor more advertisements during streaming of dynamic, adaptive andnon-sequentially assembled video. The method includes a step ofreceiving a set of preference data associated with a user frompre-defined selection criteria and a set of user authentication data.The pre-defined selection criteria are associated with a digitallyprocessed repository of videos. Further, the method includes anotherstep of fetching the one or more tagged videos from the digitallyprocessed repository of videos. The one or more tagged videos arerelated to the set of preference data of the user. The method includesyet another step of fragmenting each tagged video of the one or moretagged videos into the one or more tagged fragments. Going further, themethod includes yet another step of segregating one or more mappedfragments of the one or more tagged fragments into one or more logicalsets of mapped fragments. The method includes yet another step of miningsemantic context information from each mapped fragment of the one ormore mapped fragments and each logical set of mapped fragments of theone or more logical sets of mapped fragments. Furthermore, the methodincludes yet another step of clustering the one or more logical sets ofmapped fragments into corresponding one or more logical clusters ofmapped fragments. The method includes yet another step of allocating oneor more advertisement slots in memory reserved for assembled video.Furthermore, the method includes yet another step of inserting at leastone type of the one or more advertisements in each allocatedadvertisement slot of the one or more advertisement slots. The methodincludes yet another step of assembling at least one of the one or morelogical clusters of mapped fragments and each of the one or moreadvertisements present in each advertisement slots. The assembled videois assembled in a pre-defined order of preference. Further, the one ormore tagged videos are fetched based on a correlation of a set of tagswith the set of preference data of the user. The set of tags isassociated with each tagged video of the one or more tagged videos.Furthermore, each tagged video is fragmented into the one or more taggedfragments. Each tagged fragment is characterized by a pre-determinedinterval of time. Accordingly, each tagged video is fragmented based onsegmentation of the tagged video for each pre-determined interval oftime. Going further, the one or more mapped fragments are segregatedbased on a positive mapping of keywords from the set of preference datawith the set of tags. The set of tags is associated with each taggedfragment of the one or more tagged fragments. The semantic contextinformation includes object specific context information and scenespecific context information of each mapped fragment and each logicalset of mapped fragments Moreover, the one or more advertisements areinserted based on mining of semantic context information, analysis ofthe set of preference data and a dynamic set of conditions. The one ormore advertisements are inserted in the one or more advertisement slotsin the real time. Each logical cluster of mapped fragments is assembledbased on the analysis of the set of preference data and the semanticcontext information.

BRIEF DESCRIPTION OF THE FIGURES

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1A illustrates an interaction of a user and and one or morepublishers with an advertisement serving system, in accordance with anembodiments of the present disclosure;

FIG. 1B illustrates the interaction of the user with the advertisementserving system, in accordance with another embodiment of the presentdisclosure;

FIG. 1C illustrates the interaction of the one or more publishers withthe advertisement serving system, in accordance with yet anotherembodiment of the present disclosure;

FIG. 1D illustrates the interaction between the user and the one or morepublishers associated with the advertisement serving system, inaccordance with yet another embodiment of the present disclosure;

FIG. 1E illustrates the interaction between a live media server and theadvertisement serving system, in accordance with yet another embodimentof the present disclosure;

FIG. 2 illustrates an example of serving one or more advertisements in areal time, dynamic, adaptive and non-sequentially assembled video;

FIG. 3 illustrates a block diagram of the advertisement serving system,in accordance with various embodiments of the present disclosure;

FIG. 4 illustrates a flow chart for serving the one or moreadvertisements during streaming of the dynamic, adaptive andnon-sequentially assembled video, in accordance with various embodimentsof the present disclosure; and

FIG. 5 illustrates a block diagram of a computing device, in accordancewith various embodiments of the present disclosure.

It should be noted that the accompanying figures are intended to presentillustrations of exemplary embodiments of the present disclosure. Thesefigures are not intended to limit the scope of the present disclosure.It should also be noted that accompanying figures are not necessarilydrawn to scale.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present technology. It will be apparent, however,to one skilled in the art that the present technology can be practicedwithout these specific details. In other instances, structures anddevices are shown in block diagram form only in order to avoid obscuringthe present technology.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the present technology. The appearance of the phrase “in oneembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by some embodiments andnot by others. Similarly, various requirements are described which maybe requirements for some embodiments but not other embodiments.

Moreover, although the following description contains many specifics forthe purposes of illustration, anyone skilled in the art will appreciatethat many variations and/or alterations to said details are within thescope of the present technology. Similarly, although many of thefeatures of the present technology are described in terms of each other,or in conjunction with each other, one skilled in the art willappreciate that many of these features can be provided independently ofother features. Accordingly, this description of the present technologyis set forth without any loss of generality to, and without imposinglimitations upon, the present technology.

FIG. 1A illustrates an interaction of a user 102 a and one or morepublishers 106 with an advertisement serving system 114 and one or moread servers 110, in accordance with an embodiments of the presentdisclosure. The one or more ad servers 110 provide one or moreadvertisements for a real time insertion in advertisement slots presentbetween the assembled video. In an example, the user 102 a and each ofone or more publishers 106 interact with the advertisement servingsystem 114 based on a pay per view model. In another example, the user102 a and each of the one or more publishers 106 interact with theadvertisement serving system 114 based on a subscription based model. Inyet another example, the interaction the advertisement serving system114 with the one or more ad servers 110 is based on a pay per viewmodel. In yet another example, the interaction of with the advertisementserving system 114 with the one or more ad servers 110 is based on arevenue sharing model. Further, the interactive environment includes oneor more communication devices 102, a communication network 104, the oneor more publishers 106, the one or more ad servers 110 and a main server112.

The user 102 a is associated with the one or more communication devices102. Each of the one or more communication devices 102 may be anysuitable device with at least a display, a storage unit and networkconnectivity. In an embodiment of the present disclosure, each of theone or more communication devices 102 is a portable communicationdevice. Example of the portable communication device includes a laptop,a smart phone, a tablet and the like. For example, the smartphone may bean Apple smartphone, an Android smartphone, a Windows smartphone and thelike. In another embodiment of the present disclosure, each of the oneor more communication devices 102 is a fixed communication device.Examples of the fixed communication device include a desktop, aworkstation PC and the like. Each of the one or more communicationdevices 102 runs on an operating system. In general, the operatingsystem provides an interface for the user 102 a to interact withhardware of each of the one or more communication devices 102 and otherconnected devices. In an example, the operating system installed in theone or more communication devices 102 is a Windows based operatingsystem. In another example, the operating system installed in the one ormore communication devices 102 is a Mac based operating system. In yetanother embodiment of the present disclosure, the operating systeminstalled in the one or more communication devices 102 is a Linux basedoperating system. In yet another example, the operating system installedin the one or more communication devices 102 is a mobile operatingsystem. Example of the mobile operating system includes but may not belimited to Android operating system, Apple iOS, Symbian based operatingsystem, BADA operating system and blackberry operating system.

The one or more communication devices 102 are connected to the mainserver 112 through the communication network 104. In general, thecommunication network 104 is a part of a network layer responsible forconnection of two or more communication devices. Further, thecommunication network 104 may be any type of network. In an embodimentof the present disclosure, the type of communication network 104 is awireless mobile network. In another embodiment of the presentdisclosure, the type of communication network 104 is a wired networkwith a finite bandwidth. In yet another embodiment of the presentdisclosure, the type of communication network 104 is a combination ofthe wireless and the wired network for the optimum throughput of datatransmission. In yet another embodiment of the present disclosure, thetype of communication network 104 is an optical fiber high bandwidthnetwork that enables a high data rate with negligible connection drops.

The communication network 104 includes a set of channels. Each channelof the set of channels supports a finite bandwidth. The finite bandwidthof each channel of the set of channels is based on capacity of thecommunication network 104. Further, the one or more communicationdevices 102 possesses a unique machine address (hereinafter “MAC”). TheMAC uniquely identifies the identity of each of the one or morecommunication devices 102 over the communication network 104. Inaddition, the communication network 104 provides a unique identity toeach of the one or more communication devices 102. The unique identityis often referred to as an internet protocol (hereinafter “IP”) address.In general, an IP address is a unique string of numbers separated byfull stops that identify the one or more communication devices 102 usingIP to communicate over the communication network 104. The IP address ischaracterized by IP versions. In an embodiment of the presentdisclosure, the IP address assigned to the one or more communicationdevices 102 is an IPv4 address. In another embodiment of the presentdisclosure, the IP address assigned to the one or more communicationdevices 102 is an IPv6 address.

The one or more communication devices 102 accesses data over thecommunication network 104 by utilizing one or more applications. The oneor more applications include but may not be limited to a web browser, amobile application, a widget and web applets. In general, each of theone or more applications have a graphical user interface (hereinafter“GUI”) that is designed to display and fetch data from the main server112. In addition, each of the one or more applications on any of the oneor more communication devices associated with the user 102 a may providean interface for real time streaming, uploading and downloading of videofiles and audio files. The web browser installed in the one or morecommunication devices 102 may be any web browser. Example of the webbrowsers includes Google Chrome, Mozilla Firefox, Opera, UC Web, Safari,Internet Explorer and the like. In addition, the mobile applicationinstalled in at least one of the one or more communication devices 102may be based on any mobile platform. Examples of the mobile platforminclude but may not be limited to Android, iOS Mobile, Blackberry andBada.

Further, the one or more ad servers 110 are associated with one or moreadvertisers. Each ad server of the one or more ad servers 110 is aprovider of advertisements. Each advertiser of the one or moreadvertisers is a brand promoter. In addition, each advertiser is apromoter of brands, services and the like. Each ad server is manually orprogrammatically configured to manage serving of at least oneadvertisement present in an advertisement database of one or moreadvertisement databases 110 a. Each advertisement database of the one ormore advertisement databases 110 a includes one or more advertisements.Each advertisement may be present in any format. In an embodiment of thepresent disclosure, each advertisement is present in each advertisementdatabase of the one or more advertisement databases 110 a in a videoformat. In another embodiment of the present disclosure, eachadvertisement is present in each advertisement database of the one ormore advertisement databases 110 a in a text based format. In yetanother embodiment of the present disclosure, each advertisement ispresent in each advertisement database of the one or more advertisementdatabases 110 a in a static image format. In yet another embodiment ofthe present disclosure, each advertisement is present in eachadvertisement database of the one or more advertisement databases 110 ain an animated GIF format. In yet another embodiment of the presentdisclosure, each advertisement is present in each advertisement databaseof the one or more advertisement databases 110 a in a flash format.

Each advertisement video present in the one or more advertisementdatabases 110 may be tagged by at least one of one or moreadministrators and one or more advertisers. In an embodiment of thepresent disclosure, each advertisement is tagged with ad tags. The adtags may include but not limited to brand name, brand ambassador, brandtaglines and category of brand. Each advertisement database of the oneor more advertisement databases 110 a is connected to the main server112 through the communication network 104.

Each of the one or more communication devices 102 and the one or morepublishers 106 are connected to the main server 112. In an embodiment ofthe present disclosure, the main server 112 interacts with the one ormore communication devices 102 and the one or more ad servers 110through the communication network 104 (as shown in FIG. 1B). In anotherembodiment of the present disclosure, the main server 112 interacts withthe one or more publishers 106 and the one or more ad servers 110through the communication network 104 (as shown in FIG. 1C). In yetanother embodiment of the present disclosure, the main server 112interacts with the one or more ad servers 110 and requests of the one ormore communication devices 102 through the one or more publishers 106(as shown in FIG. 1D).

The user 102 a and each of the one or more publishers 106 are arequestor of service from the main server 112. Each publisher of the oneor more publishers 106 may be any website, web application, mobileapplication, third party applications and the like. Each publisher maybe managed by a media content provider. In an example, XYZ is a newsnetwork and a broadcaster of news on television and online platform. Thepublisher of XYZ news may be a web based platform, mobile app basedplatform or any individual content provider of media content. In anotherexample, the publisher may be an individual or group providing videos tothe advertisement serving system 112. Each of the one or more publishers106 may be associated with a publisher database of the one or morepublisher databases 108. Each publisher database of the one or morepublisher databases 108 is a database of a digitally processedrepository of videos. Each publisher of the one or more publishers 106is registered on the main server 112.

The main server 112 provides a platform for a real time placement of theone or more advertisements between one or more slots present whileassembling of the assembled video. The platform is available for videostreaming services for the user 102 a and each of the one or morepublishers 106. The platform may be a web platform, mobile applicationplatform, mobile web platform and the like. In addition, the main server112 dynamically handles requests of video placement of the one or moreadvertisements. Each request for placement of each advertisement isinitiated by corresponding advertiser of the one or more advertisers110. Moreover, the main server 112 includes the advertisement servingsystem 114, a first database 116 and a second database 118. Theadvertisement serving system 114 services the request of the user 102 aand each of the one or more publishers 106 in the real time.

The first database 116 is a proprietary database. The first database 116includes a set of user authentication data and a user profile associatedwith the user 102 a. Also, the first database 116 includes a set ofpublisher authentication data and a publisher profile associated witheach publisher of the one or more publishers 106. The user 102 a isidentified uniquely by the set of user authentication data. The set ofuser authentication data includes an email address of the user 102 a, abio-data of the user 102 a, an authentication key, a physical locationand a standard time and time zone of login. The bio data of the user 102a may include full name, nickname, chronological age, gender and thelike. In an embodiment of the present disclosure, the first database 114is an encrypted database. In another embodiment of the presentdisclosure, the first database 114 is an unencrypted database.

In addition, the first database 116 stores records of advertisementinteraction behavior related to each registered user in the userprofile. In an embodiment of the present disclosure, the advertisementinteraction behavior corresponds to a click on the advertisement by theuser 102 a. In another embodiment of the present disclosure, theadvertisement interaction behavior corresponds to an impression viewedby the user 102 a. In yet another embodiment of the present disclosure,the advertisement interaction behavior corresponds to a sale of productadvertised in corresponding advertisement by the user 102 a. Further,the first database 116 stores records of past advertisement interactionbehavior in corresponding user profile.

Further, the second database 118 is a database of digital processedrepository of videos. The second database 118 stores one or more taggedvideos. Each tagged video is virtually divisible into one or more taggedfragments. Each tagged video in the second database 118 is associatedwith a genre and a title. Examples of the genre include but may not belimited to sports, comedy, horror, drama, adventure, science fiction andautobiography. Also, each video may be associated with a popularityindex and a number of views. In addition, each video is characterized bya set of technical specifications and non-technical specifications. Theset of technical specifications include encoding format, frame rate, bitrate, frame height, frame width, pixel density, video resolution, sizeof video and the like. Each video may have different set of technicalspecifications. Each video in the second database 118 may have anyencoding format. In an embodiment of the present disclosure, theencoding format is MPEG-4. In another embodiment of the presentdisclosure, the encoding format is FLV. In yet another embodiment of thepresent disclosure, the encoding format is AVI. In yet anotherembodiment of the present disclosure, the encoding format is 3GP. In yetanother embodiment of the present disclosure, the encoding format isderived from proprietary codec. Moreover, the set of non-technicalspecifications include duration of video, a time reference associatedwith each video, the genre of video and the like.

Each video is tagged with one or more tags of a set of tags. The set oftags may correspond to a context of video, location reference in video,famous persons, events, genres, date, time and the like. In an example,a video of Moto GP race event is divisible into a lap of one or morelaps. Each lap corresponds to a relative position of each racer in racechart. Each section may be tagged with the top racer of each lap. Inanother example, a video of interview of Mike Tyson is divisible intopersonal life, social life, professional life, struggles, success,events, etc. Each section of the interview of Mike Tyson can be taggedbased on context of discussion.

The digital repository of videos in the second database 118 is updatedwith the one or more tagged videos from one or more sources. The one ormore sources may include third party video content providers, the one ormore publishers 106, the one or more advertisers, one or more sponsorsand the like. Each publisher is a platform that uploads tagged videos tothe digital repository of videos in the main server 112. The platform ofeach publisher may include a web based platform, a mobile applicationbased platform, a web application based platform and the like.Additionally, the digital repository of videos may be updated andmanaged by the platform administrators. In an embodiment of the presentdisclosure, each video is manually tagged by the one or moreadministrators. In another embodiment of the present disclosure, the oneor more administrators associated with operations of the main server 112tag each video based on voice instructions. In yet another embodiment ofthe present disclosure, each video may be tagged based on speechrendering and analysis. In yet another embodiment of the presentdisclosure, each video is automatically tagged by the advertisementserving system 112. The automatic tagging of each video is done based oncontext mining and supervised digital fingerprinting of a set of frames.In yet another embodiment of the present disclosure, each video may betagged by proprietary software and algorithms. In addition, each videomay be tagged by media agency, advertiser, creative agency and the like.Each tag of the set of tags may be rated for ranking each tag andimproving search efficiency.

Going further, the set of tags for each video may be updated based onreal time determination of frequently used tags, frequently searchedtags and less used tags. In addition, the set of tags for each video maybe updated based on dynamic meta-tagging. In an embodiment of thepresent disclosure, the metadata and meta-tagging for each tagged videomay performed according to MPEG 7 standard. The MPEG 7 standard is alsocalled as Multimedia Content Description Interface. The set of tags foreach video may be updated based on incremental machine learning on theset of tags and the metadata for each tagged video. For example, a videoon Sachin may be tagged with Sachin, master blaster, legend, god ofcricket, and the like. The advertisement serving system 114 maydetermine the most used keyword to refer to content on Sachin. Let ussuppose, in due course of 1 year, the advertisement serving system 114determines that Sachin is frequently searched with “King of Cricket”tag. The advertisement serving system 114 updates the database of theset of tags associated with Sachin. In addition, the tags will beassociated with any other video currently discussed in the publicdomain. If the name of Sachin surfaces in any new content related to anyaward show, then the tags will be automatically attached with the awardshow video too. The advertisement serving system 114 may present a Ganttchart of set of tags that are temporally classified based on occurrenceswithin search queries and preferences of the users.

The updated set of tags may be determined based on feature detection andcorrelation in a specific quadrant of one or more frames of the taggedvideos. For example, a 10 minute tagged video having a frame rate of 30fps may be processed by selecting 1 key frame per second and performingfeature detection. The feature detection may be based on incrementalmachine learning. Examples of the feature detection includes but may notbe limited to face detection, object detection, motion detection, textdetection, moving object detection and the like.

The main server 112 provides the platform to the user 102 a, each of theone or more ad servers 110 and each of the one or more publishers 106.The platform may correspond to any one of a website, mobile application,web application, mobile browser based platform. In an embodiment of thepresent disclosure, the platform is a subscription based paid platform.In another embodiment of the present disclosure, the platform is a payper view based paid platform. In yet another embodiment of the presentdisclosure, the platform is a free access, single registration and loginbased platform. The platform provides a video on demand service and anadvertisement serving service. Further, the platform includes but maynot be limited to a media player, a list of thumbnails of the one ormore tagged videos, recommendation panel, account panel, search panel,preference panel. The pre-defined selection criteria includes but maynot be limited to a set of intervals of video broadcast, a physicallocation of the user 102 a, an identified name of celebrity andcategories of video. The pre-defined selection criteria are based ondates, time zones, days, seasons, physical locations, occasions,identified names, video genres and the like. The set of intervals ofvideo broadcast corresponds to a time reference in the video. Forexample, the user 102 a may be provided with criteria to view all thenews aired between 4:00 PM to 4:15 PM of a specific day. In an example,the physical location may be used to narrow down content relevant to thephysical location. The user 102 a may like to watch videos relevant tothe physical location. The physical location may be derived through manytechniques. In an embodiment of the present disclosure, the physicallocation is derived from the global positioning system (hereinafter“GPS”) module present in at least one of the one or more communicationdevices 102 associated with the user 102 a. In another embodiment of thepresent disclosure, the physical location is derived from manualselection of the physical location from a pre-defined list of locationsby the user 102 a. In yet another embodiment of the present disclosure,the physical location is derived from internet service provider'sserver's location.

The user 102 a enters the set of authentication data through the atleast one of the one or more communication devices 102. Eachcommunication device of the one or more communication devices 102 isregistered with the main server 112. The user 102 a logs in or registerseach communication device of the one or more communication devices 102with the main server 112. The user 102 a provides a set of preferencedata through at least one of the one or more communication devices 102to the main server 112. The set of preference data is a subset of thepre-defined selection criteria provided to the user 102 a. Theadvertisement serving system 114 is configured to receive the set ofpreference data and fetch the one or more tagged videos associated withthe set of preference data of the user 102 a from the second database118. In addition, the advertisement serving system 114 is configured tofragment, segregate, cluster and assemble logical clusters or mappedfragments of one or more fetched videos to obtain an assembled video.Further, the advertisement serving system 114 is configured to allocateone or more ad slots in memory reserved for the assembled video. Theadvertisement serving system 114 is configured to insert at least one ormore advertisements from the one or more advertisement databases 110 ain the one or more ad slots. Further, the advertisement serving system114 is configured to transcode each assembled video into a pre-definedformat and streams the assembled video to the user 102 a.

Further, the advertisement serving system 114 receives the set ofpreference data associated with the user 102 a. The set of preferencedata is selected by the user 102 a from the pre-defined selectioncriteria. The set of preference data corresponds to the digitallyprocessed repository of videos. In addition, the advertisement servingsystem 114 receives the set of user authentication data. Theadvertisement serving system 114 compares the set of authentication datacorresponding to the user 102 a with the set of authentication data inthe first database 116. The advertisement serving system 114 allows fora login based on a positive comparison of received set of authenticationdata with the set of the user authentication data present in the firstdatabase 116. In an embodiment of the present disclosure, theadvertisement serving system 112 automatically handles contentmanagement associated with the set of preference data and the set ofuser authentication data. In another embodiment of the presentdisclosure, the content management associated with the set of preferencedata of the user 102 a and the set of user authentication data ismanually handled by the one or more administrators. Each of the one ormore administrators handles the content management by utilizing acontent management tool. The content management corresponds tomanagement of the user profile, streaming of the assembled video,editing and updating pre-defined selection criteria, editing pages inthe user interface and the like.

The advertisement serving system 114 creates the user profile based on areceived set of user authentication data, the set of preference data andthe real time user viewing and selection behavior. The real time viewingand selection behavior corresponds to dynamic variation in thepreferences of the user during due course of one or more active sessionsof user on the video assembling and advertisement serving platform. Inan embodiment of the present disclosure, the user profile is createdbased on a request from the user 102 a. In another embodiment of thepresent disclosure, the user profile is created automatically by theadvertisement serving system 114. The set of authentication data of theuser 102 a is stored in the user profile present in the first database116. In addition, the advertisement serving system 114 adds theadvertisement interaction behavior of the user 102 a in the userprofile. Furthermore, the advertisement serving system 114 fetches theone or more tagged videos related to the set of preference data of theuser 102 a from the digitally processed repository of videos. Theadvertisement serving system 114 fetches the one or more tagged videosbased on a correlation of the set of tags associated with each taggedvideo of the one or more tagged videos with the set of preference dataassociated with the user 102 a.

The advertisement serving system 114 virtually fragments each taggedvideo of the one or more tagged videos into the one or more taggedfragments. Each tagged video is fragmented into the one or more taggedfragments and each tagged fragment is characterized by length measuredin a pre-determined interval of time. For example, the pre-determinedinterval of time is 5 seconds for each tagged fragment of a 300 secondsvideo. In an embodiment of the present disclosure, the pre-determinedinterval of time for each tagged fragment may be manually adjusted bythe one or more administrators. In another embodiment of the presentdisclosure, the pre-determined interval of time for each tagged fragmentmay be automatically adjusted by the advertisement serving system 114based on proprietary algorithms. Each tagged video is fragmented basedon segmentation of the tagged video for each pre-determined interval oftime. Also, the fragmentation of each tagged video is a virtualfragmentation in temporary memory of the main server 112.

The advertisement serving system 114 virtually segregates one or moremapped fragments of the one or more tagged fragments into one or morelogical sets of mapped fragments. In an embodiment of the presentdisclosure, the one or more mapped fragments are segregated based on apositive mapping of keywords from the set of preference data with theset of tags. The set of tags are associated with each tagged fragment ofthe one or more tagged fragments. In addition, each tagged videos of theone or more tagged videos in the second database 118 is associated witha set of metadata. In another embodiment of the present disclosure, theone or more mapped fragments are segregated based on the positivemapping of the keywords from the set of preference data with the set ofmetadata. Each logical set of mapped fragments may correspond to acommon tag from each tagged video of the one or more tagged videos.

For example, a user, say ABC provides preferences like Comedy, JimCarrey and funny to the advertisement serving system 114. Theadvertisement serving system 114 fetches one or more tagged videosrelated to Jim Carrey, Comedy and funny preferences. The advertisementserving system 114 fragments each of the one or more videos into taggedfragments. Each tagged fragment may be of 5 second duration. Theadvertisement serving system 114 may segregate the mapped fragments fromthe tagged fragments based on a positive mapping with the set ofpreference data of the user ABC.

The advertisement serving system 114 mines semantic context informationfrom each mapped fragment of the one or more mapped fragments. Inaddition, the advertisement serving system 114 mines semantic contextinformation from each logical set of mapped fragments of the one or morelogical sets of mapped fragments. The semantic context informationincludes object specific context information and scene specific contextinformation of each mapped fragment and each logical set of mappedfragments. For example, the one or more mapped fragments may beassociated with common tags of comedy, movie, Hollywood and Jim Carrey.The advertisement serving system 114 mines semantic context informationthat includes dialogues, music, location, faces and the like. Theadvertisement serving system 114 may mine sentiments of characters ineach mapped fragment from feature analysis of audio and faces. Theadvertisement serving system 114 may mine features that includegeometrical shapes, color saturation, motion of objects, scene changes,number of scenes, animations and the like. The advertisement servingsystem 114 may mine semantic context information from the each mappedfragment of the one or more mapped fragments that is comparable with theadvertisement interaction behavior of the user 102 a. The semanticcontext information may be used to serve context related advertisementsto the user 102 a in the real time.

Going further, the advertisement serving system 114 virtually clustersthe one or more logical sets of mapped fragments into one or morelogical clusters of mapped fragments. Each logical cluster of mappedfragments is derived from at least one of the one or more logical setsof mapped fragments. For example, the advertisement serving system 114virtually fetches three tagged comedy videos of Jim Carrey. Theadvertisement serving system 114 fragments each of the three taggedcomedy videos of Jim Carrey. The mapped fragments out of taggedfragments for each tagged video may be segregated into the logical setof mapped fragments. The mapped fragments for action and comedy tags inthe three videos may be segregated to obtain the logical set of mappedfragments. The logical set of mapped fragments for comedy and actiontags for each tagged video may be clustered in the logical cluster.

Further, the advertisement serving system 114 allocates the one or moreadvertisement slots in the memory reserved for the assembled video. Inan embodiment of the present disclosure, each of the one or moreadvertisement slots is determined based on a number of biddersavailable. In another embodiment of the present disclosure, each of theone or more advertisement slots is determined based on ad skippingbehavior of the user 102 a. In yet another embodiment of the presentdisclosure, each of the one or more advertisement slots is determinedbased on ad viewing behavior of the user 102 a. In yet anotherembodiment of the present disclosure, each of the one or moreadvertisement slots is determined based on advertisement interaction ofthe user 102 a. Further, the one or more advertisement slots correspondto one or more banner based slots and one or more video based slots. Inaddition, the ad slot determination is performed based on logicalsentiment and context of the video. For example, the user 102 a may bewatching assembled compilation of homeruns by Mike Trout. Theadvertisement serving system 114 may determine that sports, baseball andMike Trout as the context relevant for the insertion of advertisement inthe nearest advertisement slot. The advertisement serving system 114 mayserve advertisement in the slot that contains sports, baseball and MikeTrout as tags. Accordingly, the advertisement serving system 114 mayapply taxonomy to the ontological relationships between different set oftags, preference data, semantic context data, the set of metadata andthe tagged fragments of videos. In general, each of the one or morebanner based slots is an empty reserved space on the platform accessedby the user 102 a. The one or more banner based slots can be uploadedwith graphical advertisement, flash based advertisement, text basedadvertisement and the like. In general, each of the one or more videobased slots corresponds to empty time slots present in play time of theassembled video.

In an example, the one or more advertisers 110 purchase the one or moreadvertisement slots from the advertisement serving system 114. Theadvertisement exchange is a platform for buying and selling ofadvertisement inventory between the one or more publishers 106, theadvertisement serving system 114 and the one or more ad servers 110. Theadvertisement exchange manages each advertisement slot. Further, theadvertisement exchange works as a third party medium for efficientbuying and selling of the advertisement inventory. In addition, theadvertisement exchange provides services for trading of the one or moreadvertisement slots between a buyer and a seller. Examples of theadvertisement exchange include but may not be limited to MicrosoftAdECN, Yahoo Right Media, DoubleClick, AppNexus, OpenX and the like. Inan embodiment of the present disclosure, the one or more ad servers 110deal directly with each other without intervention of the advertisementexchange.

Going further, the one or more ad servers 110 are associated with thedemand side platform. The demand-side platform is software forpurchasing advertisements in an automated fashion. The demand sideplatform allows the one or more ad servers 110 to buy the one or moreadvertisement slots across a range of websites. Furthermore, the demandside platform is a tool that automates purchasing of the one or moreadvertisement slots on behalf of the one or more ad servers 110. The oneor more ad servers 110 use the demand side platform for setting buyingparameters of their campaigns and to monitor campaign performance.Examples of the demand side platform include but may not be limited toGoogle's Invite Media, MediaMath, Turn, DataXu, and X+1. The one or moread servers 110 programmatically or manually run a plurality of campaigngoals. The one or more ad servers 110 set one or more goals according totheir business products. The one or more advertisers aim at targetingspecific group of users interested in the product. In an embodiment ofthe present disclosure, the one or more advertisers 110 may want totarget a specific age group. For example, an advertiser X deals inselling women apparels might want to target women in the age group of20-35 and have a fixed amount of budget expenditure.

The demand side platform targets the specific group of users and makes adecision of placing the one or more advertisements in the one or moreadvertisement slots. Moreover, the demand side platform analyzes theplurality of campaign goals and the information related to the user 102a. The information related to the user 102 a may include demographicinformation, geographical information and the like. The information ofthe user 102 a may be tracked through one or more cookies. In addition,the demand side platform receives information about the one or moreadvertisement slots. The information include but may not be limited tosize of advertisements, identifier related to the one or more publishers106, the content and web traffic related to the one or more publishers106 website and the like.

The advertisement exchange facilitate the buying and selling ofadvertisement inventory or advertisement slots through the real timebidding. The real time bidding allows the one or more advertisers totake part in a real time auction facilitated by the advertisementexchange. The demand side platform decides the one or more advertisementslots that the one or more advertisers should buy. The price of eachadvertisement slot of the one or more advertisement slots is decidedthrough the real time auction. The real time auction takes place inmilliseconds before the web page loads. The demand side platform bids onbehalf of the one or more advertisers for winning each of the one ormore advertisement slots. The real time bidding process eliminates manyadvertisers from the one or more advertisers as an advertiser with ahighest bid is declared the winner of a particular advertisement slotfrom the one or more advertisement slots.

Further, the advertisement serving system 114 inserts at least one typeof the one or more advertisements in each allocated advertisement slotof the one or more advertisement slots. The one or more advertisementsare inserted based on mining of semantic context information andanalysis of the set of preference data. In addition, the one or moreadvertisement slots are inserted based on the analysis of a dynamic setof conditions. The dynamic set of conditions includes a locationassociated with the user, user behavior information and informationassociated with one or more communication devices 102. In addition, thedynamic set of condition includes cookie information, interests of theuser, digital fingerprinting information and the like. The one or moreadvertisements are inserted in the one or more advertisement slots inthe real time.

In an embodiment of the present disclosure, each of the one or moreadvertisements present in each advertisement slot bears a contentassociated with context of the logical cluster of mapped fragments. Forexample, the user 102 a requests the main server 112 for a video onfighting scenes of Brad Pitt. The advertisement serving system 114derives the user interest to be Brad Pitt. The advertisement servingsystem 114 searches for any advertisement by Brad Pitt. Theadvertisement serving system 114 inserts the video advertisement in atleast one advertisement slot.

In addition, the advertisement serving system 114 maintains anadvertisement database in the second database 118 of the main server112. The advertisement serving system 114 selects the advertisementvideo based on logical context of the neighboring logical cluster.Further, the advertisement video may be presented for call to actionobjective. For example, the user may be presented with an advertisementof a specific car and an interactive questionnaire overlaid on top ofthe advertisement video. The user answers the questionnaire and istargeted with an option for test drive. The details of the user 102 amay be transferred to an associated car dealer for arranging test drivefor the user. In addition, the interactive questionnaire may besupplemented or complemented by hotspots, images or text on videos.

In an embodiment of the present disclosure, the advertisement servingsystem 114 may automatically set duration of playing each advertisementvideo. In another embodiment of the present disclosure, the duration ofplaying each advertisement video may be manually set by the one or moreadministrators. In yet another embodiment of the present disclosure, theduration of playing each advertisement video may be set based byproprietary applications.

The advertisement serving system 114 performs auto volume leveling oneach audio segment associated with the one or more mapped fragments orlogical clusters of the mapped fragments. For example, the first logicalcluster may contain fragments having different volume levels. Theadvertisement serving system 114 may dynamically normalize volume levelson a uniform scale. In addition, the advertisement serving system 114may perform image normalization on each frame of the mapped fragments.

In an embodiment of the present disclosure, the advertisement servingsystem 114 virtually assembles at least one of the one or more logicalclusters of mapped fragments and each of one or more insertedadvertisements to obtain the assembled video. The advertisement servingsystem 114 assembles the one or more logical clusters of mappedfragments and each of the one or more inserted advertisements in apre-defined order of preference. Each logical cluster of mappedfragments is clustered based on the analysis of the set of preferencedata of the user 102 a and the semantic context information. Forexample, the user 102 a may provide preferences like adventure, NicholasCage, movie and fighting scenes. The one or more tagged video with tagsof adventure and Nicholas Cage and movie may be tagged with specificfighting scenes. The advertisement serving system 114 mines semanticcontext information from each tagged video through searching for fightsrelated keywords from rendered speeches, scene detection, objectmovement, music, speech analysis, tone analysis and the like. Thesemantic context information may be used to automatically tag, fragment,cluster or assemble videos on demand. In an embodiment of the presentdisclosure, the real time streaming of the virtually assembled logicalclusters of mapped fragments is a selective playback of section of theone or more tagged videos.

The advertisement serving system 114 removes duplicate tags from set oftags of the real time and dynamically assembled video in the temporarymemory of the main server 112. The duplicate tags along the set ofmetadata of the assembled video are flushed in the disk for fastertransmission and caching of the assembled video on differentcommunication devices.

In an embodiment of the present disclosure, the pre-defined order ofpreference is derived from the set of preference data, the user profileand the semantic context information mined from the activities of user102 a. In another embodiment of the present disclosure, the pre-definedorder of preference is derived from preferences of users with similaruser profiles and situations. In another embodiment of the presentdisclosure, the advertisement serving system 114 virtually assembles atleast one of the one or more logical clusters of mapped fragments in adynamically generated pre-defined order of preference. The dynamicallygenerated pre-defined order of preference is based on a real timeviewing and selection behavior of the user 102 a. In an embodiment ofthe present disclosure, the pre-defined order of preference correspondsto a linear and non-sequential assembling of the one or more logical setof mapped fragments. In another embodiment of the present disclosure,the pre-defined order of preference corresponds to a non-linear andnon-sequential assembling of the one or more logical set of mappedfragments. Each logical set of mapped fragments is a virtually clusteredin the temporary memory of the main server 112. In yet anotherembodiment of the present disclosure, the pre-defined order ofpreference corresponds to sequential assembling of the one or morelogical set of mapped fragments. The advertisement serving system 114presents a personalized video solution for each user 102 a.

In an example, a person, say X selects tags corresponding to sports. Theperson (X) selects tags corresponding to Mike Tyson and boxing. Inaddition, the person selects the knockout tag from the pre-definedselection criteria. The knockout moment is often an ending portion of aboxing match. The advertisement serving system 114 fetches the one ormore tagged videos associated to matches of Mike Tyson. Theadvertisement serving system 114 searches for a knockout tag in at leastone of the one or more pre-defined sections of each tagged video. Theadvertisement serving system 114 fragments each tagged video of MikeTyson into tagged fragments and segregates logical set of mappedfragments for knockout by Mike Tyson tag from other tagged clips of MikeTyson. The advertisement serving system 114 may cluster each logical setof mapped fragments to obtain logical clusters of mapped fragments. Thelogical clusters may be assembled in the real time to obtain theassembled video. The advertisement serving system 114 allocates the oneor more advertisement slots in the memory reserved for the assembledvideo. The one or more advertisement slots may be inserted with videoadvertisements of Mike Tyson or Boxing. In addition, the advertisementserving system 114 may assemble each logical cluster or mapped fragmentsand inserted advertisements. The advertisement serving system 114dynamically serves a reassembled video to the user 102 a in the realtime upon a click on any video recommendations. The advertisementserving system 114 dynamically reassembles the one or more mappedfragments or logical clusters of mapped fragments in the real time.

The assembled video is virtual aggregation of the logical cluster of thelogical set of mapped fragments that are related to the set ofpreference data of the user 102 a and the user profile. Each logicalcluster of mapped fragments is dynamically fetched and played in thepre-defined order of preference for the user 102 a.

In an embodiment of the present disclosure, the advertisement servingsystem 114 divides each advertisement slot into finer advertisementslots where tagged fragments of advertisements may be inserted. Thefiner advertisement slots may be filled dynamically based on a brandpreference criteria of the user 102 a and the semantic contextinformation. The brand preference criteria for advertisements includebut may not be limited to a brand selection, a product selection, abudget selection, a discount selection, a stock selection and the like.

In another embodiment of the present disclosure, the one or moreadvertisements in the assembled video may be replaced with a newadvertisements depending upon the real time feedback of the user 102 a.For example, the advertisement serving system 114 analyzes the adskipping behavior of the user 102 a to automatically replace theadvertisement videos with the new advertisements.

For example, the advertisement serving system 114 may determine that theuser 102 a has a chronological age of 23 from the bio data. The user'sinterests include cricket, Sachin Tendulkar, sports, action movies,wrestling and the like. The advertisement serving system 114 segregatesand fetches the one or more advertisements that may be related tocricket theme, Sachin, sports theme and the like. The one or moreadvertisements may be served selectively based on requirements of theadvertisers. For example, if Apple wants to target advertisement of aphone to all the users between age group of 18-28, the advertisementserving system 114 may take the age requirement into account forallocation of the one or more advertisement slots.

In yet another embodiment of the present disclosure, the advertisementserving system 114 may provide another platform to the one or morepublishers 106. The platform may host real time, dynamically andnon-sequentially clustered advertisement videos based on therequirements of the user 102 a. The one or more advertisements aredynamically fetched from the one or more advertisement databases 110 a.The one or more advertisement databases may include a huge set of taggedadvertisement videos. The one or more advertisements may be segregatedand analyzed to map with the preferences of the user 102 a. For example,a user 102 a interested in purchasing cameras on an ecommerce websitemay provide a budget. The advertisement serving system 114 maydynamically fetch the one or more advertisements for each camera withinthe specified budget range and may dynamically assemble the mappedfragments of the one or more advertisement videos. Additionally, theadvertisement serving system 114 may dynamically assemble tagged clipsof the one or more advertisements for a particular brand and forproducts within the specified budget upon another selection by the user102 a. In yet another embodiment of the present disclosure, theadvertisement serving system 114 may replace background music of theassembled advertisement with a common music or audio.

The user 102 a may request to stream the assembled video that includesspecific segments of 360° videos (or immersive videos), the tagged setof videos and a live video (as shown in FIG. 1E). The main server 112 isassociated with a live media server 120. The live media server 120 is ahigh bandwidth media server that is configured to stream live videos toeach communication device of the one or more communication devices 102.The advertisement serving system 114 virtually fetches and segregatesthe one or more mapped fragments of the 360° videos and the one or moretagged videos. The mapped fragments of 360° videos and mapped fragmentsof tagged videos are derived from comparison of the keywords from theset of preference data with tags of the 360° videos and traditionalvideos. In addition, the advertisement serving system 114 requests alive media server 120 for live streaming of the live video. Theadvertisement serving system 114 virtually assembles the mappedfragments of 360° videos and mapped fragments of videos. Theadvertisement serving system 114 streams the virtually assembled mappedfragments of 360° videos and the mapped fragments of videos. Inaddition, the advertisement serving system 114 switches from theassembled content to the live video received from the live media serverin the real time.

In an embodiment of the present disclosure, the advertisement servingsystem 114 determines a real time network bandwidth associated with theone or more communication devices 102 a of the user 102. Theadvertisement serving system 114 enables bandwidth based targeting ofadvertisements on the one or more communication devices 102 associatedwith the user 102 a. For example, the user 102 a may have a slow networkthat doesn't supports seamless streaming of high bitrate videos. Theadvertisement serving system 114 may allocate less advertisement slotswith lower bitrate advertisement videos inserted in the real time.

The advertisement serving system 114 transcodes the assembled video intoa pre-defined video format. The advertisement serving system 114utilizes a codec to transcode the assembled video. The assembled videois transcoded to enable adaptive bitrate streaming on each communicationdevice of the one or more communication devices 102. The assembled videois transcoded based on one or more device parameters and one or morenetwork parameters. The one or more device parameters include screensize, screen resolution, pixel density and the like. Further, the one ormore network parameters include an IP address, network bandwidth,maximum bitrate support over network, throughput, connection strength,location of requesting server and the like. In an example, the user 102a may be using a laptop with a limited bandwidth insufficient for highdefinition streaming of videos. Accordingly, the advertisement servingsystem 114 transcodes the assembled video in format up-loadable from themain server 112. In another example, the user 102 a may be using asmartphone connected to a low bandwidth network and a lower displayresolution. Accordingly, the advertisement serving system 114 transcodesthe assembled video in the format viewable for the lower displayresolution screens. Further, the advertisement serving system 114utilizes salt stack to scale up and down transcoding requirements. Thesalt stack utilizes shell scripts to execute FFMPEG in the main server112.

In an embodiment of the present disclosure, the advertisement servingsystem 114 transcodes the assembled video in 144p quality. In anotherembodiment of the present disclosure, the advertisement serving system114 transcodes the assembled video in 240p quality. In yet anotherembodiment of the present disclosure, the advertisement serving system114 transcodes the assembled video in 360p quality. In yet anotherembodiment of the present disclosure, the advertisement serving system114 transcodes the assembled video in 480p quality. In yet anotherembodiment of the present disclosure, the advertisement serving system114 transcodes the assembled video in 720p quality. In yet anotherembodiment of the present disclosure, the advertisement serving system114 transcodes the video in 1080p quality. In yet another embodiment ofthe present disclosure, the advertisement serving system 114 transcodesthe assembled video in any standard quality.

In addition, the advertisement serving system 114 trans-rates andtrans-sizes the assembled video to enable adaptive streaming for eachcommunication device of the one or more communication devices 102. Theadvertisement serving system 114 transcodes the assembled in anystandard video coding format, container and audio coding format.Examples of the video coding format includes but may not be limited toMPEG-2 Part 2, MPEG-4 Part 2, H.264 (MPEG-4 Part 10), HEVC, Theora, RealVideo RV40, VP9, and AV1. Examples of the container includes but may notbe limited to Matroska, FLV, MPEG-4 part 12, VOB, HTML and real media.Example of the audio coding format includes but may not be limited toMP3, AAC, Vorbis, FLAC, and Opus. In an embodiment of the presentdisclosure, the assembled video is in the MP4 file format. In anotherembodiment of the present disclosure, the assembled video in thematroska file format. In yet another embodiment of the presentdisclosure, the assembled video is in the AVI file format. In yetanother embodiment of the present disclosure, the assembled video is inthe FLV file format. In yet another embodiment of the presentdisclosure, the assembled video is in the 3GP file format.

The assembled video is transcoded based on an audio codec and a videocodec. The audio codec and the video codec may be any standard orproprietary codec. Example of the video codecs include but may not belimited to H.265/MPEG-H HEVC codec, H.264/MPEG-4 AVC codec, H.263/MPEG-4codec, H.263/MPEG-4 Part 2 codec, H.262/MPEG-2 codec and ACT-L3 codec.The compression performed by the video codecs on the assembled video isa lossy compression. In addition, the advertisement serving system 114utilizes a media streaming communication protocol to stream the realtime and dynamically assembled video on each of the one or morecommunication devices 102. In an embodiment of the present disclosure,the media streaming communication protocol is a HTTP live streaming(hereinafter “HLS”) protocol. In another embodiment of the presentdisclosure, the media streaming communication protocol is a MPEG baseddynamic adaptive streaming over HTTP (hereinafter “MPEG-DASH”) protocol.

The advertisement serving system 114 renders the assembled video foraddition of one or more interactive elements and a bi-directional flowto rendered video. The one or more interactive elements include forwardplayback, reverse playback, fast playback and slow playback. Inaddition, the one or more interactive elements include touch basednavigation option, swipe based navigation option, click based navigationoption, voice based navigation, and motion based navigation option andthe like.

Further, the advertisement serving system 114 updates the user profileof the user 102 a based on variation in the set of preference data inthe first database 116. In addition, the advertisement serving system114 updates the assembled video in the digitally processed repository ofvideos in the real time. In an example, the assembled video may berecommended to any other user having a similar user profile.

It may be noted that in FIG. 1A, FIG. 1B, FIG. 1C and FIG. 1D and FIG.1E, the user 102 a accesses the assembled video from the one or morecommunication devices 102 through the main server 112; however, thoseskilled in the art would appreciate that more number of users associatedwith more number of communication devices access assembled videos frommore number of main servers. It may be noted that in FIG. 1A, FIG. 1B,FIG. 1C, FIG. 1D and FIG. 1E, the main server 112 is the provider ofvideo assembling and advertisement serving service; however, thoseskilled in the art would appreciate that more number of main serverssynchronously provide video assembling and advertisement servingservice. It may be noted that in FIG. 1A, FIG. 1B, FIG. 1C, FIG. 1D andFIG. 1E, the one or more communication devices 102 is connected to themain server 112 through the communication network 104; however, thoseskilled in the art would appreciate that more number of communicationdevices are connected to more number of main servers through more numberof communication networks.

FIG. 2 illustrates an example of the insertion of the one or moreadvertisements in the real time, dynamic, adaptive and non-sequentiallyassembled video. In the example, the one or more tagged videos includesa first video (V1), a second video (V2) and a third video (V3). Theadvertisement serving system 114 receives the request of service fromthe user 102 a through the communication network 104. The user 102 aprovides the set of preference data to the advertisement serving system114. The advertisement serving system 114 fetches the first video (V1),the second video (V2) and the third video (V3) from the second database118. In addition, the one or more pre-defined sections of the firstvideo (V1), the second video (V2) and the third video (V3) are taggedwith the set of tags. The advertisement serving system 114 fragments andlogically clusters the first video (V1) into a first logical cluster(V1C1), a second logical cluster (V1C2), a third logical cluster (V1C3),a fourth logical cluster (V1C4), a fifth logical cluster (V1C5) and asixth logical cluster (V1C6). In addition, the advertisement servingsystem 114 fragments and logically clusters a seventh logical cluster(V1C7) and an eighth logical cluster (V1C8). Accordingly, theadvertisement serving system 114 fragments and logically clusters thesecond video (V2) into a first logical cluster (V2C1), a second logicalcluster (V2C2) and a third logical cluster (V2C3). The advertisementserving system 114 clusters a fourth logical cluster (V2C4), a fifthlogical cluster (V2C5) and a sixth logical cluster (V2C6). In addition,the advertisement serving system 114 clusters a seventh logical cluster(V2C7), an eighth logical cluster (V2C8) and a ninth logical cluster(V2C9). The advertisement serving system 114 performs similar operationson the third video (V3). The fragmentation of the first video (V1), thesecond video (V2) and third video (V3) is done for the pre-determinedinterval of time. The first set of logical clusters (V1C1-V1C8), thesecond set of logical clusters (V2C1-V2C9) and the third set of logicalclusters (V3C1-V3C6) includes 8, 9 and 6 logical clusters of fragmentsrespectively. The advertisement serving system 114 inserts a firstadvertisement (AD1), a second advertisement (AD2), a third advertisement(AD3), a fourth advertisement (AD4), a fifth advertisement (AD5) and asixth advertisement (AD6). The insertion is performed in one or moreadvertisements slots allocated in the assembled video.

The advertisement serving system 114 non-linearly, non-sequentially andvirtually assembles the selected logical cluster (V2C4, V1C2, V3C4,V1C3, V2C5, V3C1 and V1C6) and the selected advertisements (AD1, AD2,AD3, AD4, AD5 and AD6) to obtain the assembled video. The assembledvideo is transcoded into the pre-defined format by the advertisementserving system 114. The assembled video in transcoded format is streamedto the user 102 a in the real time.

FIG. 3 illustrates a block diagram 300 of the advertisement servingsystem 114, in accordance with various embodiments of the presentdisclosure. It may be noted that to explain the system elements of FIG.3, references will be made to elements of the FIG. 1A, FIG. 1B, FIG. 1C,FIG. 1D and FIG. 1E. The advertisement serving system 114 receives,fetches, fragments, allocates and inserts at least one type of the oneor more advertisements in each advertisement slot present between one ormore logical sets of mapped fragments. The advertisement serving system114 includes a reception module 302, a creation module 304, a fetchingmodule 306, a fragmentation module 308 and a segregation module 310. Theadvertisement serving system 114 includes a mining module 312, aclustering module 314, an allocation module 316, an insertion module 318and an assembling module 320. In addition, the advertisement servingsystem 114 includes a transcoding module 322, a rendering module 324 andan update engine 326.

The reception module 302 receives the set of preference data associatedwith the user 102 from the pre-defined selection criteria and the set ofuser authentication data. The predefined selection criteria correspondsto the digitally processed repository of videos (as discussed indetailed description of FIG. 1A). Further, the creation module 304creates the user profile that corresponds to the received set of userauthentication data and the set of preference data. The user profileincludes the set of preference data segregated on the basis ofpre-defined selection criteria, the set of user authentication data, thepast set of preference data, a physical access location of the user 102a and a bio data of the user 102 a. The set of user authentication dataincludes an email address, a bio data of the user 102 a, anauthentication key, a physical location and a time of request of video(as discussed in detailed description of FIG. 1A).

The fetching module 306 fetches the one or more tagged videos related tothe set of preference data of the user 102 a from the digitallyprocessed repository of videos. The one or more tagged videos arefetched based on the correlation of the set of tags associated with eachvideo of the one or more tagged videos with the set of preference dataassociated with the user 102 a (as discussed in detailed description ofFIG. 1A). Further, the fragmentation module 308 fragments each taggedvideo of the one or more tagged videos into the one or more taggedfragments. Each tagged video is fragmented into the one or more taggedfragments and each tagged fragment is characterized by thepre-determined interval of time. Each tagged video is fragmented basedon segmentation of the tagged video for each pre-determined interval oftime (as discussed in detailed description of FIG. 1A). The segregationmodule 310 segregates the one or more mapped fragments of the one ormore tagged fragments into the one or more logical sets of mappedfragments. The one or more mapped fragments are segregated based on thepositive mapping of keywords from the set of preference data with theset of tags associated with each tagged fragment of the one or moretagged fragments.

Further, the mining module 312 mines the semantic context informationfrom each mapped fragment of the one or more mapped fragments. Inaddition, the mining module 312 mines the semantic context informationfrom each logical set of mapped fragments of the one or more logicalsets of mapped fragments. The semantic context information includes theobject specific context information and the scene specific contextinformation of each mapped fragment and each logical set of mappedfragments (as discussed in detailed description of FIG. 1A). Further,the clustering module 314 clusters the one or more logical sets ofmapped fragments into the one or more logical clusters of mappedfragments.

The allocation module 316 allocates one or more advertisement slots inthe memory reserved for the assembled video (as discussed in thedetailed description of FIG. 1A). The insertion module 318 inserts atleast one type of the one or more advertisements in each allocatedadvertisement slot of the one or more advertisement slots. The one ormore advertisements are inserted based on the mined semantic contextinformation, the analysis of the set of preference data and a dynamicset of conditions. The one or more advertisements are inserted in theone or more advertisement slots in the real time. The one or moreadvertisements are a type of the video advertisement and the banneradvertisement. The video advertisement and the banner advertisement areinserted simultaneously. The one or more advertisement slots areallocated based on at least one of the advertisement skipping behaviorof the user 102 a and the past interactivity of the user 102 a with theone or more advertisements. In addition, the one or more advertisementslots are allocated based on the advertisement preference manuallyselected by the user 102 a and the like (as discussed in the detaileddescription of FIG. 1A). Furthermore, the assembling module 320assembles at least one of the one or more logical clusters of mappedfragments and each of the one or more advertisements present in eachadvertisement slots to obtain the assembled video. The assembling module320 assembles at least one of the one or more logical clusters of mappedfragments and each of the one or more advertisements in thepre-determined order of preference. Each logical cluster of mappedfragments is assembled based on the analysis of the set of preferencedata and the semantic context information (as discussed in the detaileddescription of FIG. 1A).

Further, the transcoding module 322 transcodes the assembled video intothe pre-defined video format. The transcoding module utilized the codecto transcode the assembled video. The assembled video is transcoded toenable adaptive bitrate streaming on each of the one or morecommunication devices 102. The adaptive bitrate streaming is based onthe one or more device parameters and the one or more networkparameters. The one or more device parameters include screen size,screen resolution, pixel density and the like. The one or more networkparameters include the IP address, network bandwidth, maximum bitratesupport over network, throughput, connection strength and location ofrequesting server.

The rendering module 324 renders the assembled video for addition of theone or more interactive elements and bi-directional flow (as discussedin detailed description of FIG. 1A). In addition, the update engine 326updates the assembled video in the digitally processed repository ofvideos Also, the update engine 326 updates the dynamic set ofconditions, the set of authentication data and the user profile of theuser 102 a in the real time. The user profile is updated based on thevariations in the set of preference data.

FIG. 4 illustrates a flow chart 400 for serving the one or moreadvertisements during streaming of the dynamic, adaptive andnon-sequentially assembled video, in accordance with various embodimentsof the present disclosure. It may be noted that to explain the processsteps of flowchart 400, references will be made to the system elementsof the FIG. 1A, FIG. 1B, FIG. 1C, FIG. 1D, FIG. 1E and FIG. 3. It mayalso be noted that the flowchart 400 may have lesser or more number ofsteps.

The flowchart 400 initiates at step 402. Following step 402, at step404, the reception module 302 receives the set of preference dataassociated with the user 102 from the pre-defined selection criteria andthe set of user authentication data. The predefined selection criteriacorrespond to the digitally processed repository of videos. At step 406,the fetching module 306 fetches the one or more tagged videos related tothe set of preference data of the user 102 a from the digitallyprocessed repository of videos. The one or more tagged videos arefetched based on the correlation of the set of tags associated with eachvideo of the one or more tagged videos with the set of preference dataassociated with the user 102 a. At step 408, the fragmentation module308 fragments each tagged video of the one or more tagged videos intothe one or more tagged fragments. Each tagged video is fragmented intothe one or more tagged fragments and each tagged fragment ischaracterized by the pre-determined interval of time. Each tagged videois fragmented based on segmentation of the tagged video for eachpre-determined interval of time. At step 410, the segregation module 310segregates the one or more mapped fragments of the one or more taggedfragments into the one or more logical sets of mapped fragments. The oneor more mapped fragments are segregated based on the positive mapping ofkeywords from the set of preference data with the set of tags associatedwith each tagged fragment of the one or more tagged fragments. At step412, the mining module 312 mines the semantic context information fromeach mapped fragment of the one or more mapped fragments. In addition,the mining module 312 mines the semantic context information from eachlogical set of mapped fragments of the one or more logical sets ofmapped fragments. The semantic context information includes the objectspecific context information and the scene specific context informationof each mapped fragment and each logical set of mapped fragments. Atstep 414, the clustering module 314 clusters the one or more logicalsets of mapped fragments into the one or more logical clusters of mappedfragments. At step 416, the allocation module 316 allocates one or moreadvertisement slots in the memory reserved for the assembled video. Atstep 418, the insertion module 318 inserts at least one type of the oneor more advertisements in each allocated advertisement slot of the oneor more advertisement slots. The one or more advertisements are insertedbased on the mined semantic context information, the analysis of the setof preference data and a dynamic set of conditions. The one or moreadvertisements are inserted in the one or more advertisement slots inthe real time. At step 420, the assembling module 320 assembles at leastone of the one or more logical clusters of mapped fragments and each ofthe one or more advertisements present in each advertisement slots toobtain the assembled video. The assembling module 320 assembles at leastone of the one or more logical clusters of mapped fragments and each ofthe one or more advertisements in the pre-determined order ofpreference. Each logical cluster of mapped fragments is assembled basedon the analysis of the set of preference data and the semantic contextinformation. The flowchart 400 terminates at step 422.

It may be noted that the flowchart 400 is explained to have above statedprocess steps; however, those skilled in the art would appreciate thatthe flowchart 400 may have more/less number of process steps which mayenable all the above stated embodiments of the present disclosure.

FIG. 5 illustrates a block diagram of a computing device 500, inaccordance with various embodiments of the present disclosure. Thecomputing device 500 includes a bus 502 that directly or indirectlycouples the following devices: memory 504, one or more processors 506,one or more presentation components 508, one or more input/output (I/O)ports 510, one or more input/output components 512, and an illustrativepower supply 514. The bus 502 represents what may be one or more buses(such as an address bus, data bus, or combination thereof). Although thevarious blocks of FIG. 5 are shown with lines for the sake of clarity,in reality, delineating various components is not so clear, andmetaphorically, the lines would more accurately be grey and fuzzy. Forexample, one may consider a presentation component such as a displaydevice to be an I/O component. Also, processors have memory. Theinventors recognize that such is the nature of the art, and reiteratethat the diagram of FIG. 5 is merely illustrative of an exemplarycomputing device 500 that can be used in connection with one or moreembodiments of the present invention. Distinction is not made betweensuch categories as “workstation,” “server,” “laptop,” “hand-helddevice,” etc., as all are contemplated within the scope of FIG. 5 andreference to “computing device.”

The computing device 500 typically includes a variety ofcomputer-readable media. The computer-readable media can be anyavailable media that can be accessed by the computing device 500 andincludes both volatile and nonvolatile media, removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer storage media andcommunication media. The computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules or other data. Thecomputer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by the computing device 500. The communicationmedia typically embodies computer-readable instructions, datastructures, program modules or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of any ofthe above should also be included within the scope of computer-readablemedia.

Memory 504 includes computer-storage media in the form of volatileand/or nonvolatile memory. The memory 504 may be removable,non-removable, or a combination thereof. Exemplary hardware devicesinclude solid-state memory, hard drives, optical-disc drives, etc. Thecomputing device 500 includes one or more processors that read data fromvarious entities such as memory 504 or I/O components 512. The one ormore presentation components 508 present data indications to a user orother device. Exemplary presentation components include a displaydevice, speaker, printing component, vibrating component, etc. The oneor more I/O ports 510 allow the computing device 500 to be logicallycoupled to other devices including the one or more I/O components 512,some of which may be built in. Illustrative components include amicrophone, joystick, game pad, satellite dish, scanner, printer,wireless device, etc.

The present disclosure has several advantages over the prior art. Thepresent disclosure provides a solution for real time mapping of userpreference behavior. The mapping of the user preference behaviorfacilitates in identifying relevant content for the user. The presentdisclosure facilitates clustering of clips corresponding to multiplevideos having same tags. The assembled video provides a one videosolution to varied user preferences. The present disclosure provides amethod efficient in mining and attaching tags corresponding to multiplesections of the video. The assembled video solves tedious video editingwork of publishers. The present disclosure facilitates a seamlessviewing experience bundled with personalized video solution within asingle assembled video for the users. The present solution saves theswitching and selection and sorting time of user by presenting aseamless single video having multiple segments that are related to thepreferences of the user.

The foregoing descriptions of specific embodiments of the presenttechnology have been presented for purposes of illustration anddescription. They are not intended to be exhaustive or to limit thepresent technology to the precise forms disclosed, and obviously manymodifications and variations are possible in light of the aboveteaching. The embodiments were chosen and described in order to bestexplain the principles of the present technology and its practicalapplication, to thereby enable others skilled in the art to best utilizethe present technology and various embodiments with variousmodifications as are suited to the particular use contemplated. It isunderstood that various omissions and substitutions of equivalents arecontemplated as circumstance may suggest or render expedient, but suchare intended to cover the application or implementation withoutdeparting from the spirit or scope of the claims of the presenttechnology.

While several possible embodiments of the invention have been describedabove and illustrated in some cases, it should be interpreted andunderstood as to have been presented only by way of illustration andexample, but not by limitation. Thus, the breadth and scope of apreferred embodiment should not be limited by any of the above-describedexemplary embodiments.

1. A computer-implemented method for serving one or more advertisementsduring streaming of dynamic, adaptive and non-sequentially assembledvideo, the method comprising: receiving at an advertisement servingsystem with a processor, a set of preference data associated with a userfrom a pre-defined selection criteria and a set of user authenticationdata, wherein the pre-defined selection criteria corresponds to adigitally processed repository of videos; fetching at the advertisementserving system with the processor, one or more tagged videos related tothe set of preference data of the user from the digitally processedrepository of videos, wherein the one or more tagged videos beingfetched based on a correlation of a set of tags associated with eachvideo of the one or more tagged videos with the set of preference dataassociated with the user; fragmenting at the advertisement servingsystem with the processor, each tagged video of the one or more taggedvideos into one or more tagged fragments, wherein each tagged videobeing fragmented into the one or more tagged fragments, wherein eachtagged fragment being characterized by a pre-determined interval of timeand wherein each tagged video being fragmented based on segmentation ofthe tagged video for each pre-determined interval of time; segregatingat the advertisement serving system with the processor, one or moremapped fragments of the one or more tagged fragments into one or morelogical sets of mapped fragments, wherein the one or more mappedfragments being segregated based on a positive mapping of keywords fromthe set of preference data with the set of tags associated with eachtagged fragment of the one or more tagged fragments; mining at theadvertisement serving system with the processor, semantic contextinformation from each mapped fragment of the one or more mappedfragments and each logical set of mapped fragments of the one or morelogical sets of mapped fragments, wherein the semantic contextinformation comprises an object specific context information and scenespecific context information of each mapped fragment and each logicalset of mapped fragments; and clustering at the advertisement servingsystem with the processor, the one or more logical sets of mappedfragments into corresponding one or more logical clusters of mappedfragments; allocating at the advertisement serving system with theprocessor, one or more advertisement slots; inserting at theadvertisement serving system with the processor, at least one type ofthe one or more advertisements in each allocated advertisement slot ofthe one or more advertisement slots, wherein the one or moreadvertisements being inserted based on the mining of the semanticcontext information, an analysis of the set of preference data and adynamic set of conditions and wherein the one or more advertisementsbeing inserted in the one or more advertisement slots in a real time;and assembling at the advertisement serving system with the processor,at least one of the one or more logical clusters of mapped fragments andeach of the one or more advertisements present in each advertisementslot in a pre-defined order of preference to obtain an assembled video,wherein each logical cluster of mapped fragments being assembled basedon the analysis of the set of preference data and the semantic contextinformation.
 2. The computer-implemented method as recited in claim 1,further comprising creating at the advertisement serving system with theprocessor, a user profile corresponding to the received set of userauthentication data and the set of preference data, wherein the userprofile comprises the set of preference data segregated on a basis ofthe pre-defined selection criteria, the set of user authentication data,a past set of preference data, a physical access location of the userand a bio data of the user and wherein the set of user authenticationdata comprises an email address, the bio data of the user, anauthentication key, a physical location and a time of request of video.3. The computer-implemented method as recited in claim 1, furthercomprising transcoding at the advertisement serving system with theprocessor, the assembled video into a pre-defined video format, whereinthe assembled video being transcoded to enable adaptive bitratestreaming based on one or more device parameters and one or more networkparameters, wherein the one or more device parameters comprises screensize, screen resolution and pixel density and wherein the one or morenetwork parameters comprises an IP address, network bandwidth, maximumbitrate support over network, throughput, connection strength andlocation of requesting server.
 4. The computer-implemented method asrecited in claim 1, further comprising rendering at the advertisementserving system with the processor, the assembled video for adding one ormore interactive elements and bi-directional flow.
 5. Thecomputer-implemented method as recited in claim 1, further comprisingupdating at the advertisement serving system with the processor, theassembled video in the digitally processed repository of videos, theuser profile of the user based on variations in the set of preferencedata, the dynamic set of conditions and the set of user authenticationdata in a real time.
 6. The computer-implemented method as recited inclaim 1, wherein the dynamic set of conditions comprises a locationassociated with the user, user behavior information, informationassociated with one or more communication devices, cookie information,interests of the user and digital fingerprinting information.
 7. Thecomputer-implemented method as recited in claim 1, wherein the one ormore advertisements being a type of video advertisement and a banneradvertisement, wherein the video advertisement and the banneradvertisement being inserted simultaneously and wherein the one or moreadvertisement slots being allocated based on at least one of anadvertisement skipping behavior of the user, a past interactivity of theuser with the one or more advertisements and an advertisement preferencemanually selected by the user.
 8. The computer-implemented method asrecited in claim 1, wherein the set of user authentication datacomprises an email address, a bio-data of the user, an authenticationkey, a physical location, a standard time and time zone of login.
 9. Thecomputer-implemented method as recited in claim 1, wherein thepredefined selection criteria being based on date, time zone, day,season, physical location, occasion, an identified name and a videogenre.
 10. The computer-implemented method as recited in claim 1,wherein the predefined order of preference being derived from the set ofpreference data, the semantic context information, the user profile ofthe user and user profiles of any user having similar preferences. 11.The computer-implemented method as recited in claim 1, wherein eachtagged video of the one or more tagged videos being manually tagged byat least one of one or more publishers.
 12. The computer-implementedmethod as recited in claim 1, wherein each tagged video of the one ormore tagged videos being manually tagged by at least one of one or moresystem administrators.
 13. The computer-implemented method as recited inclaim 1, wherein each tagged video of the one or more tagged videosbeing tagged based on voice instructions of one or more systemadministrators.
 14. The computer-implemented method as recited in claim1, wherein each tagged video of the one or more tagged videos beingtagged based on mining from audio rendering and analysis.
 15. A computersystem comprising: one or more processors; and a memory coupled to theone or more processors, the memory for storing instructions which, whenexecuted by the one or more processors, cause the one or more processorsto perform a method for serving one or more advertisements duringstreaming of dynamic, adaptive and non-sequentially assembled video, themethod comprising: receiving at an advertisement serving system, a setof preference data associated with a user from a pre-defined selectioncriteria and a set of user authentication data, wherein the pre-definedselection criteria corresponds to a digitally processed repository ofvideos; fetching at the advertisement serving system, one or more taggedvideos related to the set of preference data of the user from thedigitally processed repository of videos, wherein the one or more taggedvideos being fetched based on a correlation of a set of tags associatedwith each video of the one or more tagged videos with the set ofpreference data associated with the user; fragmenting at theadvertisement serving system, each tagged video of the one or moretagged videos into one or more tagged fragments, wherein each taggedvideo being fragmented into the one or more tagged fragments, whereineach tagged fragment being characterized by a pre-determined interval oftime and wherein each tagged video being fragmented based onsegmentation of the tagged video for each pre-determined interval oftime; segregating at the advertisement serving system, one or moremapped fragments of the one or more tagged fragments into one or morelogical sets of mapped fragments, wherein the one or more mappedfragments being segregated based on a positive mapping of keywords fromthe set of preference data with the set of tags associated with eachtagged fragment of the one or more tagged fragments; mining at theadvertisement serving system, semantic context information from eachmapped fragment of the one or more mapped fragments and each logical setof mapped fragments of the one or more logical sets of mapped fragments,wherein the semantic context information comprises an object specificcontext information and scene specific context information of eachmapped fragment and each logical set of mapped fragments; and clusteringat the advertisement serving system, the one or more logical sets ofmapped fragments into corresponding one or more logical clusters ofmapped fragments; allocating at the advertisement serving system, one ormore advertisement slots; inserting at the advertisement serving system,at least one type of the one or more advertisements in each allocatedadvertisement slot of the one or more advertisement slots, wherein theone or more advertisements being inserted based on the mining of thesemantic context information, an analysis of the set of preference dataand a dynamic set of conditions and wherein the one or moreadvertisements being inserted in the one or more advertisement slots ina real time; and assembling at the advertisement serving system, atleast one of the one or more logical clusters of mapped fragments andeach of the one or more advertisements present in each advertisementslot in a pre-defined order of preference to obtain an assembled video,wherein each logical cluster of mapped fragments being assembled basedon the analysis of the set of preference data and the semantic contextinformation.
 16. The computer system as recited in claim 15, furthercomprising creating at the advertisement serving system, a user profilecorresponding to the received set of user authentication data and theset of preference data, wherein the user profile comprises the set ofpreference data segregated on a basis of the pre-defined selectioncriteria, the set of user authentication data, a past set of preferencedata, a physical access location of the user and a bio data of the userand wherein the set of user authentication data comprises an emailaddress, the bio data of the user, an authentication key, a physicallocation and a time of request of video.
 17. The computer system asrecited in claim 15, further comprising updating at the advertisementserving system, the assembled video in the digitally processedrepository of videos, the user profile of the user based on variationsin the set of preference data, the dynamic set of conditions and the setof user authentication data in a real time.
 18. The computer system asrecited in claim 15, further comprising transcoding at the advertisementserving system, the assembled video into a predefined video format,wherein the assembled video being transcoded to enable adaptive bitratestreaming based on one or more device parameters and one or more networkparameters, wherein the one or more device parameters comprises screensize, screen resolution and pixel density and wherein the one or morenetwork parameters comprises an IP address, network bandwidth, maximumbitrate support over network, throughput, connection strength andlocation of requesting server. 19.-20. (canceled)